Results 1  10
of
50
Clustering of the SelfOrganizing Map
, 2000
"... The selforganizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a lowdimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quant ..."
Abstract

Cited by 170 (1 self)
 Add to MetaCart
The selforganizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a lowdimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered. In this paper, different approaches to clustering of the SOM are considered. In particular, the use of hierarchical agglomerative clustering and partitive clustering usingmeans are investigated. The twostage procedurefirst using SOM to produce the prototypes that are then clustered in the second stageis found to perform well when compared with direct clustering of the data and to reduce the computation time.
Multiresolution, objectoriented fuzzy analysis of remote sensing data for GISready information
 ISPRS Journal of Photogrammetry and Remote Sensing
"... for GISready information ..."
A Proposal on Reasoning Methods in Fuzzy RuleBased Classification Systems
, 1997
"... Fuzzy RuleBased Systems have been succesfully applied to pattern classification problems. In this type of classification systems, the classical Fuzzy Reasoning Method (FRM) classifies a new example with the consequent of the rule with the greatest degree of association. By using this reasoning meth ..."
Abstract

Cited by 58 (19 self)
 Add to MetaCart
Fuzzy RuleBased Systems have been succesfully applied to pattern classification problems. In this type of classification systems, the classical Fuzzy Reasoning Method (FRM) classifies a new example with the consequent of the rule with the greatest degree of association. By using this reasoning method, we lose the information provided by the other rules with different linguistic labels which also represent this value in the pattern attribute, although probably to a lesser degree. The aim of this paper is to present new FRMs which allow us to improve the system performance, maintaining its interpretability. The common aspect of the proposals is the participation, in the classification of the new pattern, of the rules that have been fired by such pattern. We formally describe the behaviour of a general reasoning method, analyze six proposals for this general model, and present a method to learn the parameters of these FRMs by means of Genetic Algorithms, adapting the inference mechanism ...
An Evolutionary Immune Network for Data Clustering
 Proceedings of 6th Brazilian Symposium on Neural Networks (SBRN 2000
, 2000
"... This paper explores basic aspects of the immune system and proposes a novel immune network model with the main goals of clustering and filtering redundant data from problems described by a set of discrete samples. It is not our concern to reproduce with confidence any immune phenomenon, but to sh ..."
Abstract

Cited by 36 (1 self)
 Add to MetaCart
This paper explores basic aspects of the immune system and proposes a novel immune network model with the main goals of clustering and filtering redundant data from problems described by a set of discrete samples. It is not our concern to reproduce with confidence any immune phenomenon, but to show that immune concepts can be used to develop novel computational tools for data processing. As important results of our model, the network evolved will be capable of reducing redundancy, describing data structure, shapes and their cluster interrelations. The data clustering approach will be will be implemented in association with a statistical technique, and the network performance will be illustrated using two benchmark problems. The paper is concluded with a tradeoff between the proposed network and artificial neural networks. 1. Introduction The vertebrate immune system has several useful mechanisms from the viewpoint of information processing, and some powerful models have be...
Statistical strategies for avoiding false discoveries in metabolomics and related experiments
, 2006
"... Many metabolomics, and other highcontent or highthroughput, experiments are set up such that the primary aim is the discovery of biomarker metabolites that can discriminate, with a certain level of certainty, between nominally matched ‘case ’ and ‘control ’ samples. However, it is unfortunately ve ..."
Abstract

Cited by 20 (5 self)
 Add to MetaCart
Many metabolomics, and other highcontent or highthroughput, experiments are set up such that the primary aim is the discovery of biomarker metabolites that can discriminate, with a certain level of certainty, between nominally matched ‘case ’ and ‘control ’ samples. However, it is unfortunately very easy to find markers that are apparently persuasive but that are in fact entirely spurious, and there are wellknown examples in the proteomics literature. The main types of danger are not entirely independent of each other, but include bias, inadequate sample size (especially relative to the number of metabolite variables and to the required statistical power to prove that a biomarker is discriminant), excessive false discovery rate due to multiple hypothesis testing, inappropriate choice of particular numerical methods, and overfitting (generally caused by the failure to perform adequate validation and crossvalidation). Many studies fail to take these into account, and thereby fail to discover anything of true significance (despite their claims). We summarise these problems, and provide pointers to a substantial existing literature that should assist in the improved design and evaluation of metabolomics experiments, thereby allowing robust scientific conclusions to be drawn from the available data. We provide a list of some of the simpler checks that might improve one’s confidence that a candidate biomarker is not simply a statistical artefact, and suggest a series of preferred tests and visualisation tools that can assist readers and authors in assessing papers. These tools can be applied to individual metabolites by using multiple univariate tests performed in parallel across all metabolite peaks. They may also be applied to the validation of multivariate models. We stress in
Fuzzy MLP based expert system for medical diagnosis, Fuzzy Sets and Systems 65 (2=3
, 1994
"... A fuzzy MLP model, developed by the author, is used as a connectionist expert system for diagnosing hepatobiliary disorders. It can handle uncertainty and/or impreciseness in the input as well as the output. The input to the network is modelled in terms of linguistic pisets whose centres and radii ..."
Abstract

Cited by 15 (1 self)
 Add to MetaCart
A fuzzy MLP model, developed by the author, is used as a connectionist expert system for diagnosing hepatobiliary disorders. It can handle uncertainty and/or impreciseness in the input as well as the output. The input to the network is modelled in terms of linguistic pisets whose centres and radii along each feature axis are determined from the distribution of the training data. In case of partial inputs, the model is capable of querying the user for the more important feature information, when required. Justification for an inferred decision may be produced in rule form. A comparative study of the performance of the model with other methods is also provided.
Simulating flocks on the wing: The fuzzy approach
, 2005
"... Traditionally the systematic study of animal behaviour using simulations requires the construction of a suitable mathematical model. The construction of such models in most cases requires advanced mathematical skills and exact knowledge of the studied animal's behaviour. Exact knowledge is rare ..."
Abstract

Cited by 8 (1 self)
 Add to MetaCart
Traditionally the systematic study of animal behaviour using simulations requires the construction of a suitable mathematical model. The construction of such models in most cases requires advanced mathematical skills and exact knowledge of the studied animal's behaviour. Exact knowledge is rarely available. Usually it is available in the form of the observer's linguistic explanations and descriptions of the perceived behaviour. Mathematical models thus require a transition from the linguistic description to a mathematical formula that is seldom straightforward. The substantial increase of the processing power of personal computers has had as a result a notable progress in the field of fuzzy logic. In this paper we present a novel approach to the construction of artificial animals (animats) that is based on fuzzy logic. Our leading hypothesis is, that by omitting the transition from linguistic descriptions to mathematical formulas, ethologists would gain a tool for testing the existing or forming new hypotheses about `why' and `how' animals behave the way they do.
Structured Concept Discovery: Theory and Methods
, 1994
"... The field of knowledge discovery is concerned with the theory and processes involved in finding and representing patterns and regularities previously unknown. A new generation of knowledge discovery tools now deals with structured concepts: these capture associations between relations among the comp ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
The field of knowledge discovery is concerned with the theory and processes involved in finding and representing patterns and regularities previously unknown. A new generation of knowledge discovery tools now deals with structured concepts: these capture associations between relations among the components of structured objects. This paper outlines a logic used to express structured concepts, and surveys a number of systems performing structured concept discovery. The paper concludes with a discussion of important future research directions for the field. Contents 1 Introduction and motivations 3 2 Structured concepts: theoretical foundations 3 2.1 Logical concepts : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 2.1.1 A simple example : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 5 2.2 Structured concepts : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6 2.3 Subsumption of concepts : : : : : : : : : : : : : : : : : : : : : : : : : : ...
An MLP using Hough transform based fuzzy feature extraction for Bengali script recognition
, 1999
"... We define fuzzy sets on the Hough transform of character pattern pixels from which additional fuzzy sets are synthesized using tnorms. A multilayer perceptron trained with a number of linguistic set memberships derived from these tnorms can recognize characters of Bengali scripts by their similari ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
We define fuzzy sets on the Hough transform of character pattern pixels from which additional fuzzy sets are synthesized using tnorms. A multilayer perceptron trained with a number of linguistic set memberships derived from these tnorms can recognize characters of Bengali scripts by their similarities to different fuzzy pattern classes.
Using fuzzy clustering to improve naive Bayes classifiers and probabilistic networks
 Proceedings of Ninth IEEE International Conference on Fuzzy Systems (FUZZ IEEE 2000
, 2000
"... Abstract — Although probabilistic networks and fuzzy clustering may seem to be disparate areas of research, they can both be seen as generalizations of naive Bayes classifiers. If all descriptive attributes are numeric, naive Bayes classifiers often assume an axisparallel multidimensional normal di ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
Abstract — Although probabilistic networks and fuzzy clustering may seem to be disparate areas of research, they can both be seen as generalizations of naive Bayes classifiers. If all descriptive attributes are numeric, naive Bayes classifiers often assume an axisparallel multidimensional normal distribution for each class. Probabilistic networks remove the requirement that the distributions must be axisparallel by taking covariances into account where this is necessary. Fuzzy clustering tries to find general or axisparallel distributions to cluster the data. Although it neglects the class information, it can be used to improve the result of the abovementioned methods by removing the restriction to only one distribution per class. I.